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A Convex Approach for Variational Super-Resolution

Authors Unger Markus, Pock Thomas, Werlberger Manuel, Bischof Horst
Appeared in

DAGM 2010, Annual Symposium of the German Association for Pattern Recognition

Publisher

Springer, Berlin, Heidelberg

Date  2010
Abstract

We propose a convex variational framework to compute high resolution images from a low resolution video. The image formation process is analyzed to provide to a well designed model for warping, blurring, downsampling and regularization. We provide a comprehensive investigation of the single model components. The super-resolution problem is modeled as a minimization problem in an unified convex framework, which is solved by a fast primal dual algorithm. A comprehensive evaluation on the influence of different kinds of noise is carried out. The proposed algorithm shows excellent recovery of information for various real and synthetic datasets.

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